A more fundamental approach is to design the communication system from scratch, without considering any digital interface.
Different from the pure analog modulation schemes, source signal is sampled and transmitted using modulated pulses.
However, mapping the source samples to channel inputs is in general a difficult multi-dimensional optimization problem.
Recently, it was shown that DNNs can be used to break the complexity barrier of designing JSCC for wireless image transmission [1]}.
By setting up the encoder and decoder in an autoencoder configuration with a non-trainable channel layer in between, and by using the mean-squared error (MSE) metric as the loss function between the input and the output, the DNN encoder was able to learn a function which maps input images to channel inputs, and vice versa at the decoder.
They demonstrated that the resultant JSCC encoder and decoder, called DeepJSCC, was able to surpass the performance of JPEG2000 [2]} compression followed by LDPC codes [3]} for channel coding.
Importantly, they also showed that such schemes can avoid the cliff-effect exhibited by all separation-based schemes, which is when the channel quality deteriorates below the minimum channel quality to allow successful decoding of the deployed channel code, leading to a cliff-edge drop-off in the end-to-end performance.
Since then, various works have extended this result to further demonstrate the ability to exploit channel feedback [4]} and adapt to various bandwidth requirements without retraining [5]}.
In [6]}, the viability of DeepJSCC in an orthogonal frequency division multiplexing (OFDM) system was shown, and in [7]} it was shown that such schemes can be extended to multi-user scenarios using the same decoder.
DNN-aided JSCC for wireless video transmission is studied in [8]}.
